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NLP自然语言处理库，通过软硬结合、理实结合的方式，让学生全面了解人工智能在制造业的应用与开发流程。",{"type":23,"tag":31,"props":4445,"children":4446},{"style":33},[4447],{"type":23,"tag":36,"props":4448,"children":4450},{"src":4449,"style":3864,"alt":9},"/category/information/news/banner/2026-1-27/6.jpg",[],{"type":23,"tag":31,"props":4452,"children":4453},{"style":33},[4454],{"type":23,"tag":36,"props":4455,"children":4457},{"src":4456,"style":3864,"alt":9},"/category/information/news/banner/2026-1-27/7.jpg",[],{"type":23,"tag":65,"props":4459,"children":4460},{},[4461],{"type":29,"value":4462},"作为华为开发者布道师，马欣老师还走进山东大学、山东交通学院、山东管理学院等省内高校，宣讲昇思AI框架及大模型训练微调应用，受到各校师生的广泛欢迎。同时，他赴北京师范大学参加昇思大模型培训，将前沿教学理念与技术带回课堂。截至目前，他已在省内多所高校开办过昇思讲座，在本校线下开课6次，累计384课时、250名学生；线上开课5次，同样覆盖320课时、284名学生，累计让534名学生受益于昇思相关教学。此外，马欣老师还推荐本省的多位老师担任华为开发者布道师，持续孵化昇思领域的优秀人才。",{"type":23,"tag":71,"props":4464,"children":4466},{"id":4465},"_04-实践落地以昇思为翼让育人成效见行见效",[4467],{"type":29,"value":4468},"04 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2.6版本教程的测试工作。学生们在华为云、昇思大模型平台、启智社区等环境中完成案例测试，记录报错、提出解决方案并形成测试报告，在实践中提升技术能力，也为社区发展贡献力量。",{"type":23,"tag":31,"props":4487,"children":4488},{"style":33},[4489],{"type":23,"tag":36,"props":4490,"children":4492},{"src":4491,"style":3864,"alt":9},"/category/information/news/banner/2026-1-27/9.jpg",[],{"type":23,"tag":31,"props":4494,"children":4495},{"style":33},[4496],{"type":23,"tag":36,"props":4497,"children":4499},{"src":4498,"style":3864,"alt":9},"/category/information/news/banner/2026-1-27/10.jpg",[],{"type":23,"tag":65,"props":4501,"children":4502},{},[4503],{"type":29,"value":4504},"这些实践资源最终转化为学生的成长红利，昇思社区全年不间断推出的实训营、大赛、开源实习项目，构建起全方位育人闭环。 马欣老师带领工坊学生深度参与其中，22 级学生孟飞、王琨华通过昇思技术学习斩获 ICT 大赛国赛二等奖，顺利升入齐鲁工业大学深造；23 级学生江炳昱系统掌握昇思技术并考取 HCIP 证书，实习期间成功入职本地机器人公司；24 级学生王添乐凭借昇思实践经历获职业生涯规划大赛全校第三名，在更广泛的班级教学中，马欣老师引导本校学生，在2025年一年内，考取了94项华为HCIA-AI方向认证，用人工智能职业能力，为普通学生就业赋能。“昇思社区这个大平台，为好学的学生提供了广阔的机会和知识的厚土！” 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课堂上的技术积累，最终长成了产业需要的创新产品。",{"type":23,"tag":31,"props":4511,"children":4512},{"style":33},[4513],{"type":23,"tag":36,"props":4514,"children":4516},{"src":4515,"style":3864,"alt":9},"/category/information/news/banner/2026-1-27/11.jpg",[],{"type":23,"tag":71,"props":4518,"children":4520},{"id":4519},"_05总结展望以昇思为基的ai育人之路探索",[4521],{"type":29,"value":4522},"05总结展望：以昇思为基的AI育人之路探索",{"type":23,"tag":65,"props":4524,"children":4525},{},[4526],{"type":29,"value":4527},"回顾参与华为ICT大赛教学赛的经历，马欣老师最深的感悟是“教学必须与一线产业相结合”：“传统教学局限于书本经典案例，与产业脱节严重，而大赛中的案例都采用最新技术，实现了‘产业所需即课堂所授’，加上软硬件结合的形式，极大提升了学生的学习兴趣。”同时他也清醒地认识到，现有教学案例仍有提升空间，未来需进一步深入生产一线调研，让案例更贴合实际需求。",{"type":23,"tag":65,"props":4529,"children":4530},{},[4531],{"type":29,"value":4532},"针对 “学校教学如何跟上 AI 飞速发展” 这一常见疑问，马欣老师结合自身经验给出了明确答案。他不认同 “学校教学偏理论、落后于产业实践” 的观点，反而认为教师主动对接产业界 AI 技术，就能让教学紧跟迭代步伐。“我去年就参加了昇思开发者大会、ICT 创新赛、全连接大会、昇思人工智能峰会四项相关活动，还看到上海的老师带领全班学生参观展会。” 他形象地比喻，产业是流水线，教师的角色就是 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Nature communications, 2019, 10(1): 3007.",{"title":9,"searchDepth":16,"depth":16,"links":8615},[8616,8617,8618,8619],{"id":8146,"depth":1976,"text":8152},{"id":8298,"depth":1976,"text":8304},{"id":9,"depth":1976,"text":9},{"id":8413,"depth":1976,"text":8419},"content:news:zh:2025-12-16.md","news/zh/2025-12-16.md","news/zh/2025-12-16",{"_path":8624,"_dir":7,"_draft":8,"_partial":8,"_locale":9,"title":8625,"description":8626,"date":8627,"cover":3849,"type":3850,"body":8628,"_type":43,"_id":9055,"_source":45,"_file":9056,"_stem":9057,"_extension":48},"/news/zh/2025-12-15-2","昇思人工智能框架峰会 | MindSpore Quantum量子启发求解器，助力大规模组合优化问题求解效率倍增","MindSpore 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Quantum实现了量子启发式求解器。该方案基于经典硬件模拟量子演化特性，旨在优化大规模问题的寻优效率，为解决工业级复杂问题提供了一种高性能的技术新解。",{"type":23,"tag":65,"props":8641,"children":8642},{},[8643],{"type":23,"tag":1045,"props":8644,"children":8645},{},[8646],{"type":29,"value":4029},{"type":23,"tag":65,"props":8648,"children":8649},{},[8650],{"type":23,"tag":1045,"props":8651,"children":8652},{},[8653],{"type":29,"value":8654},"量子启发式算法简介",{"type":23,"tag":65,"props":8656,"children":8657},{},[8658,8660,8665],{"type":29,"value":8659},"量子启发式算法是一种新式的算法，它源于或直接受基于",{"type":23,"tag":1045,"props":8661,"children":8662},{},[8663],{"type":29,"value":8664},"量子力学原理",{"type":29,"value":8666},"的计算方法的启发，旨在利用量子力学的独特性质（叠加态、量子纠缠和量子并行性）来改进传统算法的性能。",{"type":23,"tag":1802,"props":8668,"children":8669},{},[8670,8675],{"type":23,"tag":1806,"props":8671,"children":8672},{},[8673],{"type":29,"value":8674},"对于线性代数问题：受HHL（Harrow-Hassidim-Lloyd）算法的启发，Ewin Tang提出了量子启发算法，与已知的经典方法相比，这些算法具有指数级的性能加速。",{"type":23,"tag":1806,"props":8676,"children":8677},{},[8678],{"type":29,"value":8679},"对于组合优化问题：受量子退火或类似（量子）伊辛机启发的算法。",{"type":23,"tag":65,"props":8681,"children":8682},{},[8683],{"type":23,"tag":1045,"props":8684,"children":8685},{},[8686],{"type":29,"value":8687},"量子启发式算法的发展脉络",{"type":23,"tag":65,"props":8689,"children":8690},{},[8691],{"type":23,"tag":1045,"props":8692,"children":8693},{},[8694],{"type":29,"value":8695},"1、相干伊辛机 Coherent Ising Machine, CIM",{"type":23,"tag":1802,"props":8697,"children":8698},{},[8699,8704,8709,8714],{"type":23,"tag":1806,"props":8700,"children":8701},{},[8702],{"type":29,"value":8703},"模拟相干伊辛机 Simulated Coherent Ising Machine (SimCIM, 2019)",{"type":23,"tag":1806,"props":8705,"children":8706},{},[8707],{"type":29,"value":8708},"混沌振幅控制 Chaotic Amplitude Control (CAC, 2020)",{"type":23,"tag":1806,"props":8710,"children":8711},{},[8712],{"type":29,"value":8713},"混沌振幅反馈 Chaotic Feedback Control (CFC, 2021)",{"type":23,"tag":1806,"props":8715,"children":8716},{},[8717],{"type":29,"value":8718},"离散振幅反馈 Separated Feedback Control (SFC, 2021)",{"type":23,"tag":65,"props":8720,"children":8721},{},[8722],{"type":23,"tag":1045,"props":8723,"children":8724},{},[8725],{"type":29,"value":8726},"2、模拟分岔 Simulated Bifurcation",{"type":23,"tag":1802,"props":8728,"children":8729},{},[8730,8735,8740],{"type":23,"tag":1806,"props":8731,"children":8732},{},[8733],{"type":29,"value":8734},"绝热模拟分岔 adiabatic Simulated Bifurcation (aSB, 2019)",{"type":23,"tag":1806,"props":8736,"children":8737},{},[8738],{"type":29,"value":8739},"弹道模拟分岔 ballistic Simulated Bifurcation (bSB, 2021)",{"type":23,"tag":1806,"props":8741,"children":8742},{},[8743],{"type":29,"value":8744},"离散模拟分岔 discrete Simulated Bifurcation (dSB, 2021)",{"type":23,"tag":65,"props":8746,"children":8747},{},[8748],{"type":23,"tag":1045,"props":8749,"children":8750},{},[8751],{"type":29,"value":8752},"3、平均场退火 Mean Field Annealing",{"type":23,"tag":1802,"props":8754,"children":8755},{},[8756,8761],{"type":23,"tag":1806,"props":8757,"children":8758},{},[8759],{"type":29,"value":8760},"局域量子退火 Local Quantum Annealing (LQA, 2022)",{"type":23,"tag":1806,"props":8762,"children":8763},{},[8764],{"type":29,"value":8765},"含噪平均场退火 Mean-Field Approximate Optimization Algorithm (MFAOA, 2023)",{"type":23,"tag":65,"props":8767,"children":8768},{},[8769],{"type":23,"tag":1045,"props":8770,"children":8771},{},[8772],{"type":29,"value":8773},"量子启发式算法中的模拟分岔算法 Simulated Bifurcation",{"type":23,"tag":65,"props":8775,"children":8776},{},[8777],{"type":29,"value":8778},"模拟分岔算法其核心思想是通过模拟非线性哈密顿动力学的分岔现象来寻找伊辛模型（Ising problem）的基态，从而将组合优化问题映射为物理系统的优化问题。",{"type":23,"tag":65,"props":8780,"children":8781},{},[8782],{"type":29,"value":8783},"基于模拟哈密顿方程中的分岔过程，系统通过动态调节控制参数，使系统经历一系列动力学分岔，最终收敛到由伊辛自旋变量稳定（+-1），并得到稳定解，这个解对应原始问题的局部最优解或近似解。",{"type":23,"tag":65,"props":8785,"children":8786},{},[8787],{"type":23,"tag":1045,"props":8788,"children":8789},{},[8790],{"type":29,"value":4060},{"type":23,"tag":65,"props":8792,"children":8793},{},[8794],{"type":23,"tag":1045,"props":8795,"children":8796},{},[8797],{"type":29,"value":8798},"量子启发式算法应用场景",{"type":23,"tag":65,"props":8800,"children":8801},{},[8802],{"type":23,"tag":1045,"props":8803,"children":8804},{},[8805],{"type":29,"value":8806},"1、物流与生产调度",{"type":23,"tag":65,"props":8808,"children":8809},{},[8810],{"type":29,"value":8811},"量子启发式算法擅长解决旅行商问题、0-1背包问题等NP困难问题，为物流路径规划、生产调度提供高效方案。",{"type":23,"tag":65,"props":8813,"children":8814},{},[8815],{"type":23,"tag":1045,"props":8816,"children":8817},{},[8818],{"type":29,"value":8819},"2、通信网络",{"type":23,"tag":65,"props":8821,"children":8822},{},[8823],{"type":29,"value":8824},"量子启发式算法可应用于通信网络的优化设计，例如路由优化、资源分配等，提供网络的效率和可靠性。",{"type":23,"tag":65,"props":8826,"children":8827},{},[8828],{"type":23,"tag":1045,"props":8829,"children":8830},{},[8831],{"type":29,"value":8832},"3、蛋白质结构和药物研发",{"type":23,"tag":65,"props":8834,"children":8835},{},[8836],{"type":29,"value":8837},"蛋白质分子的折叠对接是药物研发中的重要课题，量子启发式算法可预测多种蛋白质分子旋转角度，缩短求解时间，加速药物的筛选和研发。",{"type":23,"tag":65,"props":8839,"children":8840},{},[8841,8843,8848],{"type":29,"value":8842},"MindSpore Quantum已经集成量子启发式算法模块，并提供",{"type":23,"tag":1045,"props":8844,"children":8845},{},[8846],{"type":29,"value":8847},"CPU、NPU****版本",{"type":29,"value":8849},"，适配多种硬件设备，并提供极致性能。",{"type":23,"tag":1802,"props":8851,"children":8852},{},[8853,8858,8863,8868,8873,8878,8883,8888,8893,8898,8903,8908,8913],{"type":23,"tag":1806,"props":8854,"children":8855},{},[8856],{"type":29,"value":8857},"mindquantum.algorithm.qaia.QAIA 量子退火启发式算法基类",{"type":23,"tag":1806,"props":8859,"children":8860},{},[8861],{"type":29,"value":8862},"mindquantum.algorithm.qaia.CAC 混沌振幅控制算法",{"type":23,"tag":1806,"props":8864,"children":8865},{},[8866],{"type":29,"value":8867},"mindquantum.algorithm.qaia.CFC 混沌振幅反馈算法",{"type":23,"tag":1806,"props":8869,"children":8870},{},[8871],{"type":29,"value":8872},"mindquantum.algorithm.qaia.LQA 局域量子退火算法",{"type":23,"tag":1806,"props":8874,"children":8875},{},[8876],{"type":29,"value":8877},"mindquantum.algorithm.qaia.NMFA 含噪平均场退火算法",{"type":23,"tag":1806,"props":8879,"children":8880},{},[8881],{"type":29,"value":8882},"mindquantum.algorithm.qaia.ASB 绝热模拟分叉算法",{"type":23,"tag":1806,"props":8884,"children":8885},{},[8886],{"type":29,"value":8887},"mindquantum.algorithm.qaia.BSB 弹道模拟分叉算法",{"type":23,"tag":1806,"props":8889,"children":8890},{},[8891],{"type":29,"value":8892},"mindquantum.algorithm.qaia.DSB 离散模拟分叉算法",{"type":23,"tag":1806,"props":8894,"children":8895},{},[8896],{"type":29,"value":8897},"mindquantum.algorithm.qaia.TSB 三元量化模拟分岔算法",{"type":23,"tag":1806,"props":8899,"children":8900},{},[8901],{"type":29,"value":8902},"mindquantum.algorithm.qaia.USB 均匀量化模拟分岔算法",{"type":23,"tag":1806,"props":8904,"children":8905},{},[8906],{"type":29,"value":8907},"mindquantum.algorithm.qaia.LSB 对数量化模拟分岔算法",{"type":23,"tag":1806,"props":8909,"children":8910},{},[8911],{"type":29,"value":8912},"mindquantum.algorithm.qaia.SFC 离散振幅反馈算法",{"type":23,"tag":1806,"props":8914,"children":8915},{},[8916],{"type":29,"value":8917},"mindquantum.algorithm.qaia.SimCIM 模拟相干伊辛机算法",{"type":23,"tag":65,"props":8919,"children":8920},{},[8921],{"type":23,"tag":1045,"props":8922,"children":8923},{},[8924],{"type":29,"value":4220},{"type":23,"tag":65,"props":8926,"children":8927},{},[8928],{"type":23,"tag":1045,"props":8929,"children":8930},{},[8931],{"type":29,"value":8932},"实战案例-使用量子启发式算法求解最大割问题",{"type":23,"tag":65,"props":8934,"children":8935},{},[8936],{"type":29,"value":8937},"组合优化问题是一类在有限的选项集合中找到最优解的数学问题，它有着广泛的应用，像投资组合，旅行商问题等。它的求解难度随着问题规模的增加指数增长。因此，目前还不存在高效的经典算法来求解组合优化问题。",{"type":23,"tag":65,"props":8939,"children":8940},{},[8941],{"type":29,"value":8942},"Max-Cut问题是其中一种组合优化问题，该问题需要将一个图中的顶点分成两部分，并使得两部分被切割的边最多。如下图：",{"type":23,"tag":31,"props":8944,"children":8945},{"style":33},[8946],{"type":23,"tag":36,"props":8947,"children":8949},{"src":8948,"style":39,"alt":9},"/category/information/news/banner/2025-12-15-2-1.jpg",[],{"type":23,"tag":65,"props":8951,"children":8952},{},[8953],{"type":29,"value":8954},"下面演示使用MindSpore Quantum中的量子启发式算法求解最大割问题，数据集来源于经典的GSet问题，选取G22图，其规模是2000节点，19990条边。",{"type":23,"tag":8956,"props":8957,"children":8959},"pre",{"code":8958},"# 导入需要的Python模块  \nfrom mindquantum.algorithm.qaia import DSB  \nimport numpy as np  \nimport pandas as pd  \nfrom scipy.sparse import coo_matrix  \nimport time\n\n# 数据准备# 下载数据，无向图数据集来源于GSetimport requests  \n  \ngraph_file =\"https://web.stanford.edu/~yyye/yyye/Gset/G22\"  \n  \n# 使用requests库中的get方法发送HTTP请求，将url的响应结果存入变量，再以二进制写入模式打开文件写入本地response = requests.get(graph_file)  \nopen(\"G22\", \"wb\").write(response.content)  \n  \n# 数据处理def read_gset(filename, negate=True):  \n# 读取图表graph = pd.read_csv(filename, sep=\" \")  \n# 节点的数量n_v =int(graph.columns[0])  \n# 边的数量n_e =int(graph.columns[1])  \n  \n# 如果节点和边不匹配，会抛出错误assert n_e == graph.shape[0], \"The number of edges is not matched\"  \n  \n# 将读取的数据转换为一个COO矩阵（Coordinate List Format），并返回一个稀疏矩阵  \nG = coo_matrix(  \n(  \nnp.concatenate([graph.iloc[:, -1], graph.iloc[:, -1]]),  \n(  \nnp.concatenate([graph.iloc[:, 0] -1, graph.iloc[:, 1] -1]),  \nnp.concatenate([graph.iloc[:, 1] -1, graph.iloc[:, 0] -1]),  \n),  \n),  \nshape=(n_v, n_v),  \n)  \nif negate:  \nG =-G  \n  \nreturn G\n\n  \n\nG = read_gset(\"./G22\")\n\nstart_time = time.time()  \nsolver = DSB(G, batch_size=100, n_iter=1000, backend=\"npu-float32\")  \nsolver.update()  \ncut = solver.calc_cut()  \nend_time = time.time()\n\n  \n\nprint(f\"G22 MAXCut is : {max(cut)}\\nuse time:{end_time-start_time}\")\n",[8960],{"type":23,"tag":8961,"props":8962,"children":8963},"code",{"__ignoreMap":9},[8964],{"type":29,"value":8958},{"type":23,"tag":65,"props":8966,"children":8967},{},[8968],{"type":29,"value":8969},"输出：",{"type":23,"tag":8956,"props":8971,"children":8973},{"code":8972},"G22 MAXCut is : 13353.0\n   use time:0.5273990631103516\n",[8974],{"type":23,"tag":8961,"props":8975,"children":8976},{"__ignoreMap":9},[8977],{"type":29,"value":8972},{"type":23,"tag":65,"props":8979,"children":8980},{},[8981,8983,8988],{"type":29,"value":8982},"可以看到，",{"type":23,"tag":1045,"props":8984,"children":8985},{},[8986],{"type":29,"value":8987},"DSB算法",{"type":29,"value":8989},"仅用0.53秒就求解出了2000节点的GSet图，该图的最大切割数在13353附近。",{"type":23,"tag":2850,"props":8991,"children":8993},{"id":8992},"使用npu加速量子启发式算法",[8994],{"type":23,"tag":1045,"props":8995,"children":8996},{},[8997],{"type":29,"value":8998},"使用NPU加速量子启发式算法",{"type":23,"tag":65,"props":9000,"children":9001},{},[9002],{"type":29,"value":9003},"上述卓越的求解速度，归功于MindSpore Quantum中利用NPU对此类量子启发式算法的显著加速。我们分别在CPU和NPU后端上运行DSB算法，求解最大割问题：",{"type":23,"tag":8956,"props":9005,"children":9007},{"code":9006},"import time  \n  \nstart_time = time.time()  \nsolver = DSB(G, batch_size=100, n_iter=1000, backend=\"cpu-float32\")  \nsolver.update()  \ncut = solver.calc_cut()  \ncpu_fp32_time = time.time() - start_time  \n  \nstart_time = time.time()  \nsolver = DSB(G, batch_size=100, n_iter=1000, backend=\"npu-float32\")  \nsolver.update()  \ncut = solver.calc_cut()  \nnpu_fp32_time = time.time() - start_time\n\nimport matplotlib.pyplot as plt# 计算加速比cpu_speedup = cpu_fp32_time / cpu_fp32_time  \nnpu_speedup = cpu_fp32_time / npu_fp32_time  \n  \n  \ndevices = [\"CPU-Float32\", \"NPU-Float32\"]  \ntimes = [cpu_fp32_time, npu_fp32_time]  \nspeedups = [cpu_speedup, npu_speedup]  \ncolors = [\"#4C72B0\", \"#DD8452\"]  \n  \n  \nplt.figure(figsize=(10, 6), dpi=100)  \n  \n# 绘制横向条形图, 表示不同后端的计算时间bars = plt.barh(devices, times, color=colors, height=0.6)  \n  \n# 添加数据标签和加速比  \nfor i, (time, speedup) inenumerate(zip(times, speedups)):  \nplt.text(  \ntime +0.05,  \ni,  \nf\"{time:.2f}s ({speedup:.1f}x)\",  \nva=\"center\",  \nfontsize=12,  \n)  \n  \nplt.title(  \n\"QAIA Performance Comparison On Different Hardware In G22\", fontsize=14, pad=20  \n)  \nplt.xlabel(\"Time(seconds)\", fontsize=12)  \nplt.xlim(0, max(times) *1.3)  \nplt.grid(axis=\"x\", linestyle=\"--\", alpha=0.7)  \nplt.tight_layout()  \n  \nplt.show()\n",[9008],{"type":23,"tag":8961,"props":9009,"children":9010},{"__ignoreMap":9},[9011],{"type":29,"value":9006},{"type":23,"tag":31,"props":9013,"children":9014},{"style":33},[9015],{"type":23,"tag":36,"props":9016,"children":9018},{"src":9017,"style":39,"alt":9},"/category/information/news/banner/2025-12-15-2-2.jpg",[],{"type":23,"tag":65,"props":9020,"children":9021},{},[9022],{"type":29,"value":9023},"可以看到，对于同样的问题，如果使用CPU进行求解，需要14.2秒才能完成，而NPU仅需0.5秒，使求解速度提升了28倍。",{"type":23,"tag":65,"props":9025,"children":9026},{},[9027],{"type":29,"value":8539},{"type":23,"tag":65,"props":9029,"children":9030},{},[9031],{"type":29,"value":8544},{"type":23,"tag":65,"props":9033,"children":9034},{},[9035,9040,9043],{"type":23,"tag":120,"props":9036,"children":9038},{"href":8550,"rel":9037},[199],[9039],{"type":29,"value":8550},{"type":23,"tag":1314,"props":9041,"children":9042},{},[],{"type":29,"value":8558},{"type":23,"tag":65,"props":9045,"children":9046},{},[9047],{"type":23,"tag":120,"props":9048,"children":9050},{"href":8564,"rel":9049},[199],[9051],{"type":29,"value":8564},{"title":9,"searchDepth":16,"depth":16,"links":9053},[9054],{"id":8992,"depth":1976,"text":8998},"content:news:zh:2025-12-15-2.md","news/zh/2025-12-15-2.md","news/zh/2025-12-15-2",{"_path":9059,"_dir":7,"_draft":8,"_partial":8,"_locale":9,"title":9060,"description":9061,"date":8627,"cover":3849,"type":3850,"body":9062,"_type":43,"_id":9420,"_source":45,"_file":9421,"_stem":9422,"_extension":48},"/news/zh/2025-12-15","昇思人工智能框架峰会 | 基于MindSpore 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d_ffn",{"type":29,"value":9344},"矩阵转变为统一的",{"type":23,"tag":6920,"props":9346,"children":9347},{},[9348],{"type":29,"value":9349},"8×d_model, d_ffn",{"type":29,"value":9351},"矩阵。",{"type":23,"tag":1806,"props":9353,"children":9354},{},[9355],{"type":29,"value":9356},"统一计算流程：路由网络输出的选择权重不再用于动态激活不同专家，而是作为加权系数直接应用于融合后的计算结果。具体而言，模型首先通过融合权重矩阵执行一次统一的前向计算，然后根据路由权重对输出进行加权组合，避免了传统的专家遍历过程。",{"type":23,"tag":65,"props":9358,"children":9359},{},[9360],{"type":29,"value":9361},"针对DeepSeekV2（DeepSeek-OCR LLM模块）的改进代码如下：",{"type":23,"tag":8956,"props":9363,"children":9365},{"code":9364},"def new_forward_for_moe(self, hidden_states):\n  batch_size, sequence_length, hidden_dim = hidden_states.shape\n  selected_experts, routing_weights = self.gate(hidden_states)\n  router_scores = torch.zeros(size=(batch_size * sequence_length, self.config.n_routed_experts), device=hidden_states.device, dtype=hidden_states.dtype)\n  # we cast back to the input dtype\n  routing_weights = routing_weights.to(hidden_states.dtype)\n  router_scores = torch.scatter_add(router_scores, -1, selected_experts, routing_weights)\n  hidden_states = hidden_states.view(-1, hidden_dim)\n  if self.config.n_shared_experts is not None:\n     shared_expert_output = self.shared_experts(hidden_states)\n    \n  hidden_w1 = torch.matmul(hidden_states, self.w1)\n  hidden_w3 = torch.matmul(hidden_states, self.w3)\n  hidden_states = self.act(hidden_w1) * hidden_w3\n  hidden_states = torch.bmm(hidden_states, self.w2) * torch.transpose(router_scores, 0, 1).unsqueeze(-1)\n  final_hidden_states = hidden_states.sum(dim=0, dtype=hidden_states.dtype)\n  if self.config.n_shared_experts is not None:\n     hidden_states = final_hidden_states + shared_expert_output\n  return hidden_states.view(batch_size, sequence_length, hidden_dim)\n",[9366],{"type":23,"tag":8961,"props":9367,"children":9368},{"__ignoreMap":9},[9369],{"type":29,"value":9364},{"type":23,"tag":65,"props":9371,"children":9372},{},[9373],{"type":29,"value":9374},"在昇思MindSpore+昇腾的软硬件协同环境中，这一技术大幅提升了DeepSeek-OCR的执行速度，相较于原版实现，推理token生成的性能提升3-4x，算力利用率由8%提升至30%+。这种基于Expert合并的加速思路，为小规模MoE模型的部署提供了一种新的优化范式，特别是在对推理延迟敏感的端侧和应用场景中具有重要价值。",{"type":23,"tag":65,"props":9376,"children":9377},{},[9378],{"type":29,"value":9379},"优化后的推理效果如视频中所示：",{"type":23,"tag":31,"props":9381,"children":9382},{"style":33},[9383],{"type":23,"tag":36,"props":9384,"children":9386},{"src":9385,"style":39,"alt":9},"/category/information/news/banner/2025-12-15-4.jpg",[],{"type":23,"tag":65,"props":9388,"children":9389},{},[9390],{"type":23,"tag":1045,"props":9391,"children":9392},{},[9393],{"type":29,"value":5113},{"type":23,"tag":65,"props":9395,"children":9396},{},[9397],{"type":23,"tag":1045,"props":9398,"children":9399},{},[9400],{"type":29,"value":9401},"总结",{"type":23,"tag":65,"props":9403,"children":9404},{},[9405],{"type":29,"value":9406},"DeepSeek-OCR与昇思MindSpore在昇腾硬件上的深度结合，标志着文档智能处理进入了一个全新的发展阶段。这一技术组合不仅展现了前沿AI模型的创新潜力，更体现了从算法、框架到硬件的全栈优化价值。",{"type":23,"tag":65,"props":9408,"children":9409},{},[9410],{"type":29,"value":9411},"展望未来，随着多模态大模型技术的持续演进和昇腾算力基础设施的不断完善，OCR模型与昇思MindSpore的深度结合将释放更大潜力。从简单的文档识别到复杂的知识抽取，从单页处理到跨文档分析，这一技术路径正在开启文档智能的新篇章，为企业数字化转型和AI普惠应用提供坚实的技术底座。",{"title":9,"searchDepth":16,"depth":16,"links":9413},[9414,9416,9417,9418,9419],{"id":9116,"depth":1976,"text":9415},"高压缩比下的精度保持：****实验表明，当文本令牌数量在视觉令牌数量的10倍以内（即压缩比\u003C10倍）时，模型可以实现97%的解码精度，即使在20倍压缩率下仍保有约60%准确率。",{"id":9127,"depth":17,"text":9133},{"id":9136,"depth":17,"text":9142},{"id":9291,"depth":17,"text":9297},{"id":9318,"depth":17,"text":9324},"content:news:zh:2025-12-15.md","news/zh/2025-12-15.md","news/zh/2025-12-15",{"_path":9424,"_dir":7,"_draft":8,"_partial":8,"_locale":9,"title":9425,"description":9426,"date":1142,"cover":3849,"type":3850,"body":9427,"_type":43,"_id":9520,"_source":45,"_file":9521,"_stem":9522,"_extension":48},"/news/zh/2025-12-12","昇思人工智能框架峰会 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V3每个层的路由专家个数高达256个，在训练实践中为了减小显存压力往往开启专家并行(EP)，将专家切分到不同的卡上。然而，当EP数大于单个节点的的NPU/GPU数量时，专家会被切分到不同节点上，在token dispatch和combine阶段，需要进行AlltoAll的机间通信。因机间带宽远小于机内带宽，此时，机间通信不可避免地成为通信性能的瓶颈。",{"type":23,"tag":65,"props":9460,"children":9461},{},[9462],{"type":29,"value":9463},"昇思MindSpore团队针对这一问题，采用跨机AllGather通信与机内AlltoAll通信相结合的方式，解决AlltoAll机间通信性能差的问题。首先将所需的tokens通过跨机AllGather同步到机间，然后在机间进行tokens的排序与AlltoAll通信。基于这种分层的通信方式降低了跨机通信数据量，有效地提升了整体通信性能。经过在DeepSeek V3 671B实训测试，在EP=16时端到端吞吐性能提升15%。机间通信合并与原始通信方案的示意如图1。",{"type":23,"tag":31,"props":9465,"children":9466},{"style":33},[9467],{"type":23,"tag":36,"props":9468,"children":9470},{"src":9469,"style":39,"alt":9},"/category/information/news/banner/2025-12-12-1.jpg",[],{"type":23,"tag":65,"props":9472,"children":9473},{},[9474],{"type":29,"value":9475},"图1. 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combine阶段的AlltoAllV，其原理如图2所示。",{"type":23,"tag":31,"props":9502,"children":9503},{"style":33},[9504],{"type":23,"tag":36,"props":9505,"children":9507},{"src":9506,"style":39,"alt":9},"/category/information/news/banner/2025-12-12-2.jpg",[],{"type":23,"tag":65,"props":9509,"children":9510},{},[9511],{"type":29,"value":9512},"昇思MindSpore通过其异构能力实现AlltoAllV收发send_list/receive_list的异构复用，将断流次数从4次降低到1次。在DeepSeek V3 671B实训测试，端到端性能提升5%。",{"type":23,"tag":65,"props":9514,"children":9515},{},[9516],{"type":29,"value":9517},"昇思MindSpore针对MoE性能提升的业界难题，成体系地采用优化技术，包括但不限于上述2项技术，构筑了昇思MindSpore面向超大规模MoE训练的高效通信底座，更多的技术介绍与交流，请关注昇思人工智能框架峰会。",{"title":9,"searchDepth":16,"depth":16,"links":9519},[],"content:news:zh:2025-12-12.md","news/zh/2025-12-12.md","news/zh/2025-12-12",{"_path":9524,"_dir":7,"_draft":8,"_partial":8,"_locale":9,"title":9525,"description":9526,"date":9527,"cover":3849,"type":3850,"body":9528,"_type":43,"_id":9721,"_source":45,"_file":9722,"_stem":9723,"_extension":48},"/news/zh/2025-12-11","昇思人工智能框架峰会 | 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flow的，五花八门包罗万象，可以说是深度学习领域的万花筒了，我们团队在开发的时候经常需要像学生时代那样对着论文一句一句过，查漏补缺，基本上大部分主流的模型架构都重新熟悉了一遍。框架方面呢，由于团队成员一开始都是习惯使用的TensorFlow/PyTorch，对于MindSpore并不熟悉，同时由于昇思MindSpore还在快速发展时期，有时候我们需要从头实现一些函数，但是由于Mindspore的开源项目暂时也不是很多，所以我们可以借鉴的代码也比较少，因此有时候大量翻阅MindSpore的API文档也是家常便饭的事情。当然了，做READ项目本身还是一个充满乐趣的事情。对我个人而言，做开源项目最大的收获是获得对于很多的算法更深的理解，同时也确确实实地在社区里留下了自己的东西。",{"type":23,"tag":71,"props":10789,"children":10790},{"id":10230},[10791],{"type":29,"value":10230},{"type":23,"tag":65,"props":10793,"children":10794},{},[10795],{"type":29,"value":10796},"由TCL工业研究院基于昇思MindSpore实现的工业AI算法库READ，专注于研发与集成基于无监督监督学习的大规模异常检测算法和模型，重点解决工业质检中小样本和数据长尾问题，通过针对不同行业的业务和数据特点，研发可在无异常（无缺陷）图像上进行自学习和自训练的异常检测技术，旨在为工业检测领域提供一个无监督异常检测的框架。",{"title":9,"searchDepth":16,"depth":16,"links":10798},[10799,10800,10801,10802,10803],{"id":10059,"depth":17,"text":10059},{"id":10064,"depth":17,"text":10064},{"id":10089,"depth":17,"text":10089},{"id":10770,"depth":17,"text":10770},{"id":10230,"depth":17,"text":10230},"content:news:zh:2023-3-28-8.md","news/zh/2023-3-28-8.md","news/zh/2023-3-28-8",{"_path":10808,"_dir":7,"_draft":8,"_partial":8,"_locale":9,"title":10809,"description":10810,"date":10054,"cover":3849,"type":3850,"body":10811,"_type":43,"_id":10877,"_source":45,"_file":10878,"_stem":10879,"_extension":48},"/news/zh/2023-3-28-9","手语教考一体机","华为与千博信息基于紫东太初开发手语多模态模型，实现手语动作与图文联动，便于初学者理解。该模型还支撑手语教考一体机开发，支持随学随练随考，推动手语标准普及，助力听障人士和爱好者学习。",{"type":20,"children":10812,"toc":10871},[10813,10817,10821,10826,10831,10836,10840,10845,10849,10854,10858,10862,10867],{"type":23,"tag":71,"props":10814,"children":10815},{"id":10059},[10816],{"type":29,"value":10059},{"type":23,"tag":71,"props":10818,"children":10819},{"id":10064},[10820],{"type":29,"value":10064},{"type":23,"tag":65,"props":10822,"children":10823},{},[10824],{"type":29,"value":10825},"全球约有2.5亿听障人群，其中大部分需要依靠手语。十六年前，第二次全国残疾人抽样调查结果显示，我国有约2780万名听力残疾者，占全国残疾人的30%以上。这一数量还在持续增长。听力障碍严重影响着这个特殊群体的生活、学习和社会交往，不仅时常制造各种尴尬与不便，而且使他们面临沟通难、就业难等一系列问题。",{"type":23,"tag":65,"props":10827,"children":10828},{},[10829],{"type":29,"value":10830},"究其原因，主要是手语知识学习的挑战。",{"type":23,"tag":65,"props":10832,"children":10833},{},[10834],{"type":29,"value":10835},"仅用手语手势交流的他们，在学习时，往往面临无法准确理解、手语词汇更新慢、看不懂其他地域的手语“方言”等难题。",{"type":23,"tag":36,"props":10837,"children":10839},{"src":10838,"style":10732,"alt":9},"/category/information/news/banner/2023-3-28-9-1.png",[],{"type":23,"tag":65,"props":10841,"children":10842},{},[10843],{"type":29,"value":10844},"就像中英文之别，手语作为一种无声的语言，在不同地区有不同的\"语法\"，比如北京人打手语，海南人可能看不懂是什么意思。",{"type":23,"tag":71,"props":10846,"children":10847},{"id":10089},[10848],{"type":29,"value":10089},{"type":23,"tag":65,"props":10850,"children":10851},{},[10852],{"type":29,"value":10853},"基于紫东太初开发的手语多模态模型，华为联合千博信息，实现了手语动作与示意图片和文字的联动，让初学者更加方便的理解手语。同时，基于手语多模态模型，千博信息打造了手语教考一体机，帮助学生随学随练随考，极大的推动手语标准的推广，帮助听障朋友以及手语爱好者更好的学习。\n研发人员让“紫东太初”三模态大模型学习了1.2万个手语词汇、50多万语法、70多万语料，基于昇思MindSpore 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